0000000000008163

AUTHOR

Sudarsan Rachuri

showing 12 related works from this author

A methodology for the semi-automatic generation of analytical models in manufacturing

2018

International audience; Advanced analytics can enable manufacturing engineers to improve product quality and achieve equipment and resource efficiency gains using large amounts of data collected during manufacturing. Manufacturing engineers, however, often lack the expertise to apply advanced analytics, relying instead on frequent consultations with data scientists. Furthermore, collaborations between manufacturing engineers and data scientists have resulted in highly specialized applications that are not relevant to broader use cases. The manufacturing industry can benefit from the techniques applied in these collaborations if they can be generalized for a wide range of manufacturing probl…

Optimization0209 industrial biotechnologySupport Vector MachineGeneral Computer ScienceProcess (engineering)Computer sciencemedia_common.quotation_subjectResource efficiencyComputerApplications_COMPUTERSINOTHERSYSTEMS02 engineering and technology020901 industrial engineering & automationManufacturing0202 electrical engineering electronic engineering information engineeringAdvanced analytics[INFO]Computer Science [cs]Quality (business)Use caseMillingmedia_commonGenetic AlgorithmArtificial Neural-Networkbusiness.industrySystemsGeneral EngineeringModel-basedNeural networkRegressionManufacturing engineeringProduct (business)ManufacturingSurface-RoughnessAnalytics020201 artificial intelligence & image processingDynamic Bayesian NetworksPerformance indicatorFault-DiagnosisPredictionbusinessComputers in Industry
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DEXML: A First Step Toward a UML Based Implementation Framework for PLCS

2011

Data exchange specifications not only must be broad and general to achieve acceptance, but also must be customizable in a controlled and interoperable manner to be useful. The Product Life Cycle Support (PLCS) suite of data exchange specifications (known as DEXs) uses templates to enable controlled customizability without sacrificing breadth or interoperability. DEXs are business context-specific subsets of ISO 10303 Application Protocol (AP) 239, subject to additional constraints imposed by the templates. A PLCS template defines how AP239 entities and their attributes will be instantiated using an externally-defined controlled vocabulary defined in a Reference Data Library. Template instan…

Programming languageComputer sciencebusiness.industryInteroperabilityProgrammable logic controllercomputer.file_formatcomputer.software_genreSoftwareUnified Modeling LanguageInformation modelData exchangeControlled vocabularybusinesscomputerISO 10303computer.programming_languageVolume 2: 31st Computers and Information in Engineering Conference, Parts A and B
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Product lifecycle management support: a challenge in supporting product design and manufacturing in a networked economy

2005

In this paper, we provide an overview of the changing design and manufacturing landscape in the 21st century that has come about because of IT and the changing global conditions. Based on this overview and a review of the current state of IT for PLM support in the design and manufacturing sector, we identify the areas of need for standards. A review of areas covered by standards leads us to the development of an initial typology of standards and a potential path for bringing convergence of these standards in support of PLM. We make a case throughout the paper that given the nature of the task we need to aspire to create open standards with wide participation. We conclude by arguing that the…

EngineeringProcess managementProduct designbusiness.industryInformation technologyContext (language use)Management Science and Operations ResearchManufacturing engineeringProduct lifecycleProduct life-cycle managementOpen standardNew product developmentBusiness and International ManagementSafety Risk Reliability and QualitybusinessImplementationInternational Journal of Product Lifecycle Management
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A Virtual Milling Machine Model to Generate Machine-Monitoring Data for Predictive Analytics

2015

Real data from manufacturing processes are essential to create useful insights for decision-making. However, acquiring real manufacturing data can be expensive and time consuming. To address this issue, we implement a virtual milling machine model to generate machine monitoring data from process plans. MTConnect is used to report the monitoring data. This paper presents (1) the characteristics and specification of milling machine tools, (2) the architecture for implementing the virtual milling machine model, and (3) the integration with a simulation environment for extending to a virtual shop floor model. This paper also includes a case study to explain how to use the virtual milling machin…

Engineeringbusiness.product_categoryMTConnect[ INFO ] Computer Science [cs]Process (engineering)business.industryComputerApplications_COMPUTERSINOTHERSYSTEMSData generatorPredictive analyticsSTEPIndustrial engineeringMachine toolManufacturing dataMTConnectMonitoring dataData analyticsData generatorData analysis[INFO]Computer Science [cs]businessSimulationMilling
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Automated uncertainty quantification analysis using a system model and data

2015

International audience; Understanding the sources of, and quantifying the magnitude of, uncertainty can improve decision-making and, thereby, make manufacturing systems more efficient. Achieving this goal requires knowledge in two separate domains: data science and manufacturing. In this paper, we focus on quantifying uncertainty, usually called uncertainty quantification (UQ). More specifically, we propose a methodology to perform UQ automatically using Bayesian networks (BN) constructed from three types of sources: a descriptive system model, physics-based mathematical models, and data. The system model is a high-level model describing the system and its parameters; we develop this model …

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]generic modeling environment[SPI] Engineering Sciences [physics]Computer scienceuncertainty quantificationMachine learningcomputer.software_genre01 natural sciencesData modelingSystem model[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]010104 statistics & probability03 medical and health sciences[SPI]Engineering Sciences [physics][ SPI ] Engineering Sciences [physics]Sensitivity analysis0101 mathematicsUncertainty quantification[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]030304 developmental biologyautomation0303 health sciencesMathematical modelbusiness.industryConditional probabilityBayesian networkmeta-modelMetamodelingBayesian networkProbability distributionData miningArtificial intelligencebusinesscomputer
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OntoSTEP: Enriching product model data using ontologies

2012

The representation and management of product lifecycle information is critical to any manufacturing organization. Different modeling languages are used at different lifecycle stages, for example STEP's EXPRESS may be used at a detailed design stage, while UML may be used for initial design stages. It is necessary to consolidate product information created using these different languages to build a coherent knowledge base. In this paper, we present an approach to enable the translation of STEP schema and its instances to Ontology Web Language (OWL). This gives a model-which we call OntoSTEP-that can easily be integrated with any OWL ontologies to create a semantically rich model. As an examp…

computer.internet_protocolbusiness.industryModeling languageComputer scienceProgramming languageProtégéOntology (information science)computer.software_genreComputer Graphics and Computer-Aided DesignIndustrial and Manufacturing EngineeringOWL-SComputer Science ApplicationsMetamodelingProduct lifecycleKnowledge baseUnified Modeling LanguageData miningbusinesscomputercomputer.programming_languageComputer-Aided Design
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Model-based Engineering for the Integration of Manufacturing Systems with Advanced Analytics

2016

To employ data analytics effectively and efficiently on manufacturing systems, engineers and data scientists need to collaborate closely to bring their domain knowledge together. In this paper, we introduce a domain-specific modeling approach to integrate a manufacturing system model with advanced analytics, in particular neural networks, to model predictions. Our approach combines a set of meta-models and transformation rules based on the domain knowledge of manufacturing engineers and data scientists. Our approach uses a model of a manufacturing process and its associated data as inputs, and generates a trained neural network model as an output to predict a quantity of interest. This pape…

0209 industrial biotechnologyProcess (engineering)Computer scienceneural network02 engineering and technology[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI][SPI]Engineering Sciences [physics]020901 industrial engineering & automationComputer-integrated manufacturing0202 electrical engineering electronic engineering information engineering[ SPI ] Engineering Sciences [physics][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]Meta-modelArtificial neural networkbusiness.industrymeta-modelData scienceNeural networkPredictive modelingMetamodelingWorkflowAnalyticsData analyticsData analysisDomain knowledgemanufacturing process020201 artificial intelligence & image processingManufacturing processbusinessSoftware engineeringpredictive modeling
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Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML)

2017

International audience; This paper describes Gaussian process regression (GPR) models presented in predictive model markup language (PMML). PMML is an extensible-markup-language (XML) -based standard language used to represent data-mining and predictive analytic models, as well as pre- and post-processed data. The previous PMML version, PMML 4.2, did not provide capabilities for representing probabilistic (stochastic) machine-learning algorithms that are widely used for constructing predictive models taking the associated uncertainties into consideration. The newly released PMML version 4.3, which includes the GPR model, provides new features: confidence bounds and distribution for the pred…

Computer sciencecomputer.internet_protocol02 engineering and technologycomputer.software_genreIndustrial and Manufacturing EngineeringArticleSet (abstract data type)[SPI]Engineering Sciences [physics]Kriging020204 information systems0202 electrical engineering electronic engineering information engineeringUncertainty quantificationRepresentation (mathematics)predictive model markup language (PMML)Probabilistic logicdata miningPredictive analyticsXMLComputer Science Applicationspredictive analyticsControl and Systems EngineeringPredictive Model Markup Languagestandards020201 artificial intelligence & image processingData miningcomputerXMLGaussian process regression
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Automated Uncertainty Quantification Through Information Fusion in Manufacturing Processes

2017

International audience; Evaluation of key performance indicators (KPIs) such as energy consumption is essential for decision-making during the design and operation of smart manufacturing systems. The measurements of KPIs are strongly affected by several uncertainty sources such as input material uncertainty, the inherent variability in the manufacturing process, model uncertainty, and the uncertainty in the sensor measurements of operational data. A comprehensive understanding of the uncertainty sources and their effect on the KPIs is required to make the manufacturing processes more efficient. Towards this objective, this paper proposed an automated methodology to generate a hierarchical B…

Computer scienceinjection molding02 engineering and technologycomputer.software_genreIndustrial and Manufacturing Engineering[SPI]Engineering Sciences [physics]GME0202 electrical engineering electronic engineering information engineeringUncertainty quantificationuncertaintyautomationhierarchicalbusiness.industryBayesian network020207 software engineeringmeta-modelAutomationComputer Science ApplicationsMetamodelingInformation fusionBayesian networkControl and Systems Engineeringsemantic020201 artificial intelligence & image processingData miningbusinesscomputer
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A Model for Capturing Product Assembly Information

2005

The important issue of mechanical assemblies has been a subject of intense research over the past several years. Most electromechanical products are assemblies of several components, for various technical as well as economic reasons. This paper provides an object-oriented definition of an assembly model called the Open Assembly Model (OAM) and defines an extension to the NIST Core Product Model (NIST-CPM). The assembly model represents the function, form, and behavior of the assembly and defines both a system level conceptual model and associated hierarchical relationships. The model provides a way for tolerance representation and propagation, kinematics representation, and engineering anal…

Engineeringbusiness.industryProcess (engineering)media_common.quotation_subjectConceptual model (computer science)Data structureComputer Graphics and Computer-Aided DesignIndustrial and Manufacturing EngineeringComputer Science ApplicationsUnified Modeling LanguageCore productAssembly modellingSystems engineeringbusinessFunction (engineering)computerSoftwareEngineering analysiscomputer.programming_languagemedia_commonJournal of Computing and Information Science in Engineering
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Toward a Reference Architecture for Archival Systems

2013

Long-term preservation of product data is imperative for many organizations. A product data archive should be designed to ensure information accessibility and understanding over time. Approaches such as the Open Archival Information System (OAIS) Reference Model and the Audit and Certification of Trustworthy Digital Repositories (ACTDR) provide a framework for conceptually describing and evaluating archives. These approaches are generic and do not focus on particular contexts or content types. Enterprise architecture provides a way to describe systems in their potentially complex environments.

Enterprise architecture frameworkWorld Wide WebOpen Archival Information SystemComputer scienceApplications architectureSolution architectureEnterprise architectureReference architectureData architectureView model
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Towards a Reference Architecture for Archival Systems: Use Case With Product Data

2014

Long-term preservation of product data is imperative for many organizations. A product data archive should be designed to ensure information accessibility and understanding over time. Approaches, such as the Open Archival Information System Reference Model (OAIS RM) and the Audit and Certification of Trustworthy Digital Repositories (ACTDR), provide a framework for conceptually describing and evaluating archives. These approaches are generic and do not focus on particular contexts or content types such as product data. Moreover, these approaches offer no guidance on how to formally and comprehensively describe archival systems. Such descriptions should include the business activities that a…

0209 industrial biotechnologyComputer scienceEnterprise architecture020101 civil engineering02 engineering and technologyIndustrial and Manufacturing Engineering0201 civil engineeringTerminologyWorld Wide Web[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI]020901 industrial engineering & automation[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO][INFO]Computer Science [cs]Reference architectureArchitectureReference modelComputingMilieux_MISCELLANEOUS[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]business.industryComputer Graphics and Computer-Aided Design[INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR]Computer Science ApplicationsOpen Archival Information SystemInformation model[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]Department of Defense Architecture Framework[INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]Software engineeringbusinessSoftwareJournal of Computing and Information Science in Engineering
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